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Record W2905744191 · doi:10.32920/ryerson.14662107.v1

Exploring Themes of Tibetan Cultural Identity During the Settlement Experience in Toronto, Canada

2021· preprint· en· W2905744191 on OpenAlexaffabout
Farha Akhtar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsToronto Metropolitan UniversityUniversity of Regina
Fundersnot available
KeywordsSettlement (finance)AcculturationRefugeeIdentity (music)Context (archaeology)CitizenshipCultural identityGender studiesPolitical scienceSociologyGeographyEthnologyImmigrationSocial scienceArchaeologyLawPolitics

Abstract

fetched live from OpenAlex

This study explores both existing research as well as emerging themes in the area of Tibetan exile communities. Previous research examining the settlement experiences of Tibetan refugees in Europe, the United States and India has focused largely on the various ways in which individuals acculturate and adapt to new, culturally different environments. Out of this research, a number of themes arise such as cultural expectations, internal concepts of identity, and citizenship. Authors have suggested that Tibetan refugees during settlement are likely to adopt a version of Berry’s integration strategy of acculturation. What is lacking in these earlier studies is an exploration of the experiences of Tibetan refugees in Canada. This study is set within a Canadian context and considers other social factors that may greatly influence the settlement experience, namely barriers to settlement such as the failure to have foreign credentials recognized. Key words: Tibetan, Refugees, Settlement, Acculturation, Cultural Identity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.549

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0440.012
Scholarly communication0.0070.002
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.103
GPT teacher head0.356
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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